Augmentation is a re-weighting approach [1]}[2]}[3]}, in which accepted samples are re-weighted to represent the entire distribution.
A common way to achieve this is re-weighting according to the rejection/approval probability.
Moreover, the augmentation approach has been extended in a fuzzy way [4]}.
Parcelling is also a re-weighting approach, where the re-weighting is determined by the default probability by score-band that is adjusted by the credit modeler [5]}[3]}.
To be noted, these re-weighting methods are similar to the researches on counterfactual learning [7]}[8]}[9]}[10]}.
Counterfactual learning aims to remove data bias, in which the re-weighting of training samples is widely adopted.
